Search Results - (( pattern generation learning algorithm ) OR ( java application stemming algorithm ))
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Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…In the first and second phases, a threshold fuzzy c-means clustering algorithm as a clusterer and a pattern ensemble learning method based on the incremental genetic-based algorithms are proposed respectively. …”
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Frequent Lexicographic Algorithm for Mining Association Rules
Published 2005“…The primary concept of association rule algorithms consist of two phase procedure. In the first phase, all frequent patterns are found and the second phase uses these frequent patterns in order to generate all strong rules. …”
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Machine learning predictions of stock market pattern using Econophysics approach
Published 2025“…Hence, this research will be using Monte Carlo Simulation and identify which machine learning algorithm is suitable for predicting stock market patterns. …”
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Book Section -
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Machine Learning Approach Regarding The Classification And Prediction Of Dog Sounds: A Case Study Of South Indian Breeds
Published 2024journal::journal article -
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A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition
Published 2002“…A vector quantization model that incorporate rough sets attribute reduction and rules generation with a modified version of the K-means clustering algorithm was developed, implemented and tested as a part of a speech recognition framework, in which the Learning Vector Quantization (LVQ) neural network model was used in the pattern matching stage. …”
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Effect of chaos noise on the learning ability of back propagation algorithm in feed forward neural network
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Exploratory study of Kohonen network for human health state classification
Published 2018“…Neural Connection tool is used to generate the result based on Kohonen learning algorithm. …”
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Herbs recognition based on chemical properties using machine learning algorithm
Published 2023“…This method has demonstrated promising results in identifying herb species, and the classification method based on machine learning algorithms has proven successful in recognizing and distinguishing herb species…”
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Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation
Published 2020“…The proposed algorithm named as Box-Whisker Data Transformation considered all samples contain in a MLCC dataset in order to generate artificial samples. …”
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Analysis of Chinese patents associated with incremental clustering algorithms: A review / Archana Chaudhari
Published 2022“…Such huge data has embedded patterns and hidden information to extract and learn. …”
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Theory-guided machine learning for predicting and minimising surface settlement caused by the excavation of twin tunnels / Chia Yu Huat
Published 2024“…This is due to the data generated from the numerical model possess the pattern for the ML algorithm ease of prediction. …”
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Generation of synthetic 5G network dataset using Generative Adversarial Network (GAN)
Published 2024“…The existence of Generative Adversarial Network (GAN) helps solve the issue by generating fake data from an existing dataset to resemble real-world data to be used in training and testing of different algorithms. …”
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Conference or Workshop Item -
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Tracking student performance in introductory programming by means of machine learning
Published 2023Conference Paper -
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Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier
Published 2013“…The effect of extracting naïve Bayes classifier’s vocabulary from patterns generated by FP-Growth algorithm was explored. …”
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Underwater Image Recognition using Machine Learning
Published 2024“…It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…Social media mining is the process of collecting large datasets from user-generated content and extracting and analyzing social media interactions to recognize meaningful patterns in individual and social behavior. …”
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